Simple Linear Regression

·2023년 5월 17일
0

단순 선형 회귀

Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Importing the dataset

dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)

Training the Simple Linear Regression model on the Training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

Predicting the Test set result

y_pred = regressor.predict(X_test)

Visualising the Training set result

plt.scatter(X_train, y_train, color = 'red') 
plt.plot(X_train, regressor.predict(X_train), color='blue') # 회귀선
plt.title('Salary vs Exprerience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

Visualising the Test set results

plt.scatter(X_test, y_test, color = 'red') 
plt.plot(X_train, regressor.predict(X_train), color='blue') # 회귀선
plt.title('Salary vs Exprerience (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

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